This presentation covers best practices for implementing simple to advanced machine learning use cases on AWS. First. we will review the decision points for using services such as Amazon Lex, Amazon Polly and integration with services such as Amazon Connect. Then we will look at real use cases, optimising the customer experience with chatbots, streamlining the customer experience predicting responses with Amazon Connect. Finally, we will dive deep into the most common of these patterns and cover design and implementation considerations. (39:05)

Building Multichannel Conversational Interfaces Using Amazon Lex

In this presentation, discover how to build a multichannel conversational interface that leverages a preprocessing layer in front of Amazon Lex. This preprocessing layer can enable customers to integrate their conversational interface with external services and use multiple specialized Amazon Lex chatbots as part of an overall solution. As an example of how to integrate with an external service, learn how to integrate with Skype. Watch it in action through a chatbot demonstration with interaction through Skype messaging and voice. (1:03:22)

Delight your Retail Customers using Chatbots Powered by Amazon Lex

Today’s retail customers expect exceptional customer service and tailored solutions to their problems. Chat and voice interfaces provide retailers with new ways to interact with their customers and to provide intelligent & efficient solutions. In this session, we will build and demonstrate a chatbot powered by AI from AWS that can autonomously guide a customer through the process of reporting an undelivered or defective item & quickly offer appropriate solutions. Learn how to redefine your customer service experience by tying together Amazon Lex, AWS Lambda and AWS DynamoDB to easily add ChatBot functionality to your retail solution. (47:13)

How to Mix Amazon Lex, Amazon Lambda, and IoT to Give Life to Everyday Objects

This is a technical presentation that explains how to use Amazon Lex and Amazon Lambda to quickly prototype and deploy a serverless chatbot connected with an embedded device in order to realize an Internet of Things (IoT) application. The presentation covers how you can integrate your IoT application with Amazon Lex using AWS Lambda and the Amazon API Gateway, how to exchange session data to have a contextual conversation, and how to provide a successful bot experience. (44:18)

Using a Digital Assistant in the Enterprise for Business Productivity

Enterprises must transform at the pace of technology. Through chatbots built with Amazon Lex, enterprises are improving business productivity, reducing execution time, and taking advantage of efficiency savings for common operational requests. These include inventory management, human resources requests, self-service analytics, and even the onboarding of new employees. In this session, learn how Infor integrated Amazon Lex into their standard technology stack, with several use cases based on advisory, assistant, and automation roles deeply rooted in their expanding AI strategy. (59:58)

Building a Voice-Enabled Customer Service Chatbot Using Amazon Lex and Amazon Polly

This presentation combines use of Amazon Lex and Amazon Polly to demonstrate how to build a Help Desk chatbot that feature spoken-voice interfaces. Foundational skills are covered for those looking to enrich their applications with natural, conversational interfaces. Liberty Mutual Insurance presents on their chat platform architecture to demonstrate how they are using Amazon Lex in their organization as an employee digital assistant. (1:00:25)

This presentation covers an integration of Amazon Lex with a contact center solution. We demonstrate how an Amazon Lex chatbot can be inserted into an interactive voice response (IVR) workflow in a contact center, enabling users to interact with the chatbot using natural language. We walk through a ready-to-deploy integration that includes building the bot, setting up the IVR, and managing the call routing. We also describe the best practices for selective routing based on user intent, exchange of information between the chatbot/IVR, and handover to a human agent. (53:00)